Cognitive recording and sharing

ABSTRACT

A system and method and computer program product for cognitive recording and sharing of live events. The system includes: a sensing and transmitting device that can sense the biometric signatures of an individual; a processing unit that analyses the sensed signal and initiate a set of actions; a recording device or the like to record the event; and a networked sharing device configured to subsequently share recorded event content. The system further identifies individuals&#39; pre-cognitive inputs and additional external and internal factor input signals that are precursors to cognitive affirmation of an emotional response. These inputs will be identified, correlated, and used in training the system for subsequent identification and correlation between input factors and resulting emotional state. External factors may include: recognized emotional states, biometric inputs, and/or precognition inputs of other individuals in proximity to the subject individual. Other factors may include an individual&#39;s context.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/788,226, filed Jun. 30, 2015, the contents of which are incorporatedby reference herein in their entirety.

FIELD

This disclosure relates generally to recording of events usingtraditional recording devices such as cameras, camcorders, phones, etc.and wearable devices such as Google glass, etc., and more particularly,to a system and method that enables the recording device to adapt to anindividual user's biometric signatures and use those signatures totrigger the recording of events, tagging of key observations and sharingof the events/tags with other users.

BACKGROUND

Traditional recording devices such as cameras, camcorders, phones, etc.and wearable devices such as Google glass, etc. require the user toactively know when to record. In doing so, the user recording the eventis not able to enjoy the moment he/she is recording. Because users areactively interacting with technology they are not “present in themoment”.

SUMMARY

A system, method and computer program product to provide recordingdevice technology to adapt to an individual user's biometric signaturesand use those signatures to trigger the recording of events, tagging ofkey observations and sharing of the events/tags with a user selectedgroup. The system and method particularly will enable the user to livein the moment.

In one aspect, a system and apparatus for cognitive recording andsharing of live events is provided. The apparatus for cognitiverecording and sharing of live events comprises: a processing unit; arecording device to record a live event; one or more sensors, eachconfigured for obtaining a biometric signal data from an individual; atransmitting device for communicating the one or more biometric signalsfor receipt at the processing unit, the processing unit configured to:obtain a biometric signature of the individual based on a receivedbiometric signal data; obtain a signal representing one or more of: arecognized emotional state of, a biometric signature of, and adetermined precognition input of one or more other individuals inproximity to the individual; determine an individual's emotional statebased on the signature in combination with the obtained signals of theone or more other individuals; and record the live event by therecording device in response to the determined emotional state.

In a further aspect, there is provided a method for cognitive recordingand sharing of live events. The method comprises: receiving, at aprocessing device, a biometric signal from an individual; obtaining abiometric signature of the individual based on a received biometricsignal data; obtaining, at the processing device, a signal representingone or more of: a recognized emotional state of, a biometric signatureof, and a determined precognition input of one or more other individualsin proximity to the individual; determining, at the processing device,an individual's emotional state based on the signature in combinationwith the obtained signals of the one or more other individuals; andtriggering a recording device to record a live event responsive todetermined emotional state.

In a further aspect, there is provided a computer program product forperforming operations. The computer program product includes a storagemedium readable by a processing circuit and storing instructions run bythe processing circuit for running a method. The method is the same aslisted above.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings, in which:

FIG. 1 shows conceptually a system 10 for cognitive recording andsharing of live events in one embodiment;

FIG. 2 depicts a methodology 100 implemented by a processing unit thatin one embodiment is used to ascertain from received signals anemotional state of an individual for use in triggering a recording;

FIG. 3 depicts general operations performed by the system to train amodel for use in correlating the individual's biometric signatures andcontext to that individual's emotional state;

FIG. 4 generally depicts an analytics process used to determine how toannotate a particular recorded event for an individual, and make sharingrecommendations of the recorded content; and

FIG. 5 depicts an exemplary hardware configuration for performingmethods such as described in FIGS. 2-4 in one embodiment;

FIG. 6 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 7 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 8 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

As shown in FIG. 1, there is provided a system 10 employing methods forproviding an ability to adapt to an individual user's biometricsignatures, use those signatures to trigger the recording of an event(s)75, tag key observations and share the events/tags with a individualsand/or a user selected group. This will enable the individual 11 to“live in the moment” as the individual will no longer have to take thetime to manually record an event.

In FIG. 1, the system 10 implements elements such as one or morewearable devices 12 that are worn by individual 11 and that includessensor devices adapted for receiving and sensing biometric signals ofthat individual. The wearable devices are coupled with a transmittingdevice that can sense the biometric signatures of the individual andcommunicate the received biometric signal data as signals 15 or 16 forreceipt at a receiver and ultimately a processor or like control device25, e.g., in response to a processor query. In one embodiment, shown inFIG. 1, optionally a wearable device may include a vision device 19,e.g., a goggles or lenses (e.g., Google® glass) to generate visionsignals 15 or 16 for receipt at a processor device. Further included isa processing device (e.g., a microprocessor, FPGA or like control logicdevice) 25 that analyses the sensed signal(s) and responsivelyautomatically initiates a set of actions; and a recording device 30,such as a camera or video recorder provided in a mobile smartphonedevice or the like, configured to automatically record a live occurringevent responsive to actions or signals initiated from the processingdevice 25. In one embodiment, the processing device 25 is shown at therecording device 30 to control the intra-connected or inter-connectedcognitive sensing employed by system 10, and including: recording andprocessing functions described herein. Herein, “cognitive sensing”refers to the use of the received biometric and other input signals toascertain a cognitive and/or emotional state of the individual or otherindividuals in proximity to the first individual. Once a cognitive oremotional state of the individual is ascertained, system 10 generatessignals to trigger a recording of the live or unplanned event by therecording device.

In one embodiment, as shown in FIG. 1, the sensing and transmittingdevice could be a wearable device having the aforementionedsensors/detectors, an audio/video recorder, a motion sensor, aninfra-red camera, or the like. For example, a wearable device 12 couldsense the biometric signals from the user and/or physical gesturesand/or reactions made by the user for transmission and/or processing.

In one embodiment, sensor devices that may be worn by the user, e.g., aspart of a wearable device 12 or as an addition to, include but are notlimited to: biometric sensor devices for detecting and measuring anindividual's 11 physiological characteristics, e.g., a motion sensor, anaccelerometer, a pulse-oximeter to monitor the wearer's heart rate, abreathing rate monitor, a biochemical sensor, a particular hormone leveldetector, a skin conductance detector, a moisture detector, an infraredlight detector, or combinations thereof, or any device that could sensethe biometric signals from the user and/or physical gestures andreactions made by the user.

In one embodiment, the wearable device(s) 12, processing device 25 andrecording devices 30 may form a local network at the individual. In oneembodiment, the local network may be configured as a personal areanetwork (PAN) to provide the intra/inter-connected cognitive sensing,recording and processing functions described herein. Such a network mayemploy wired or wireless communications technologies. In one embodiment,the network may be a wireless PAN, e.g., based on the standard IEEE802.15. WPAN may employ wireless technologies such as Bluetooth orInfrared Data Association (IrDA) or Near Field Communications (NFC)technology.

In a further embodiment, the wearable devices 12 including biometricsensors and detectors, and the recording devices, may establishcommunication with a higher level network 40, e.g., an Internetweb-site, that is configured with a processing device(s) 35 to performor control the intra/inter-connected cognitive sensing, recording andprocessing functions described herein. In one embodiment, the networkedsharing infrastructure 40 may be implemented to receive and process thesensor data signals 15 transmitted from the individual's wearabledevices, or vision devices 19, or additionally receive biometric signalsor other inputs 45 from other users in close proximity to theindividual, e.g., a few meters.

For instance, in one embodiment, the individual's personal network ornetwork 40 may additionally receive biometric signals or other inputs 45from other users who may be networked with individual 11 when in closeproximity to the individual. For example, such other inputs 45 mayinclude a video recording, a cognitive and emotional state, and/orinterpreted sensor data of other networked users in the proximity of theindividual, e.g., a video recording of their expressions or hand or bodygestures, emotional state and related biometric sensor data, that thehardware processing unit 35 may use in real-time when ascertaining acognitive state of the individual 11.

Further, in one embodiment, the system may be configured to detect atrigger such as a gesture by the individual 11 to initiate a recording.As will be described, the physical gestures and responsive actioncorrelation may be made such as by training the system 10 using amachine supervised learning algorithm so that the system may learn agesture and correlate it to an action by the system 10 (e.g., start arecording of a live event).

Based on intra- and inter-cognitive sensing functions of the network 40and processing unit 35, and the real-time processing of biometricsignals of the individual and/or other users in response to a liveand/or unplanned event, signals 28 may be transmitted to a recordingdevice 30 to initiate the live event recording. For example, the systemcould detect wirelessly communicated signals 15 from biometric sensorsindicating that a heart-rate of the individual and skin moisture levelhas increased significantly in a short period of time. In response tothese received sensed signals 15 from that individual, the processor 25or 35 may ascertain a state of the individual 11, and responsivelytrigger the recording device 30 to record an event 75 that is happeningin the wearer's locality.

Thus, as shown in FIG. 1, the local processing unit 25 or processingunit 35 that analyzes the simultaneous signals received from varioussensors, may responsively issue a set of signals 28 to appropriatedevices, e.g., a camera, or smart phone or mobile phone camera 30, toeither record the on-going event taking place in the user's locality, orto stop the recording of the on-going event.

Once the event is recorded, it may be stored locally in the recordingdevice or network 40. Subsequent processing using a network cloud, e.g.,cloud computing environment 90, and particularly, using an analyticsprocessing device 50, e.g., of a web-based server device (not shown), isprovided that can automatically receive the recorded material 80 of theevent 75, and includes functionality to feed the recorded event and/orassociated data/metadata and/or aspects 85 of the recorded event 75 toone or more social networking web-sites 60 a, 60 b, etc., to make therecording available for others.

FIG. 2 shows a methodology 100 implemented by processing unit 25 eitherlocally or in the remote network location 40 that in one embodiment isused to ascertain the processes inputs for an individual. In oneembodiment, the processor unit lays idle until it receives inputs at 102when it determines that signals received are sensor signal datapertaining to the individual's biometrics (e.g., from wearable devicesor Google glasses). At 102, if biometric signals are received, themethod proceeds to 108 where there is identified a change in thebiometric signature of the individual. At 110, the process, having beenpreviously trained as a cognitive system, will correlate the determinedbiometric signature of the individual to an emotional state of thatindividual (i.e., what the user “is feeling”) and afterward return tostep 112. If at 102, no current biometric signals are received at theprocessing unit, then the method will proceed automatically to determineif any other type of inputs have been received.

That is, at 112 and 122 it contemplated that other signal inputs arereceived by the network including signals representing other internalfactors or external factors. These additional internal factors (at step112) or external factors (at step 122) are further used in determiningsaid individual's emotional state. Thus, in one embodiment, at 112, adetermination is made as to whether any internal input factors have beenreceived. If no internal factors have been received, (e.g., a change incertain hormone levels) then the process will continue to 122, FIG. 2.Otherwise, if signals representing internal factors have been receivedat 112, then the process continues at 118 where the internal factorsinput(s) are identified and processed.

In one embodiment, internal factors may include but are not limited to:hormone and biochemical changes of an individual in the time period andcontext before, during, and after an emotional state; and/orelectromagnetic field changes in close proximity to the subjectindividual and in the time period and context before, during, and afteran emotional state.

In this processing, at 118, any additional context informationpertaining to the individual that can be used in further assessing thatuser's emotional state could be used. For example, informationpertaining to the individual's locality or environment, a cognitivecontext of the individual, the individual's environment and/or justabout anything that refers to the subject individual's own uniquecontext. This context is collected, calibrated, and additionally refinedthrough training of a model. For example, a determination of anindividual's increased heart rate or blood pressure alone may not beenough, a person may just have a high resting rate or easily excitable.Using this context information in combination with inputs helps achievebetter correlation to an emotional state. Thus, at 120 the contextualand other internal factors pertaining to an individual context may beused in further correlating to the individual's emotional state. In theprocess, the internal factor inputs may be weighted in accordance with aranking or priority scheme.

Afterwards, the method returns to step 122 where a determination is madeas to whether any external input factors have been received.

In one embodiment, external factors may include but not limited to:recognized emotional states, biometric inputs, and/or precognitioninputs of other individuals in proximity to the subject individual;and/or sound frequency, volume, proximity, and novelty in proximity tothe subject individual, and/or visual light intensity, color, andmovement in proximity to the subject individual.

If no external factors have been received, then the process willcontinue to 132, FIG. 2. Otherwise, if signals representing externalfactors have been received at 122, then the process continues at 128where the external factors input(s) are identified and processed. In theprocess, the external factor inputs may be weighted in accordance with aranking or priority scheme.

For example, in one embodiment, the processing unit 25 or 40 may receivefurther signals from their recording devices, e.g., video and/or audioinformation inputs from other sources, received at the network 40. Forexample, these inputs may include signals representing biometricmeasurements of the individual wearer, or a signal(s) representing oneor more of: a recognized emotional state of, a biometric signature of,and a determined precognition input of one or more other individuals inproximity to the individual.

As a further example, a received sensor output may include a videorecording of another individual user's facial expression or physicalgestures. That is, in one embodiment, other individuals in proximity tothe user may employ wearable devices with sensors and can communicatewith the individual's network. Information from others' wearable devicesand/or identification of user moods or emotional states of others inproximity to the individual e.g., via wireless network devicecommunications, may be received as inputs. Thus, other wearers'individual sensors in addition to received effective emotional states(getting real-time information from other users who are nearby theindividual), and then getting a processed emotional state (of theindividual or a neighbor) is used in the emotional state determination.

Other “external” signal inputs may include other inputs, e.g., audiosignals associated with an event, a video of a facial expression or abodily gesture of another person in proximity to the wearer, or aninterpreted emotional state of other users nearby the individual (i.e.,what neighbors “are feeling”). At step 130, FIG. 2, the contextual andother internal factors pertaining to an individual context may be usedin further correlating to the individual's emotional state. In theprocess, the external factor inputs may additionally be weighted inaccordance with a ranking or priority scheme.

Whether external factor signals are processed or not, afterwards, themethod returns to step 132 the processing correlates each of thereceived user inputs (e.g., biometric, internal and external factors) toa user's cognitive or emotional state (e.g., agitated, scared,intrigued). In one embodiment, at 132, FIG. 2, there is performed anidentification of user moods or emotions based on the received inputs.For example, if there is received external factor input signalindicating a loud sound, and an individual's biometric signals areraised to a level indicating an emotional state (e.g., scared,intrigued, attentive), this emotional identification is a factor that isweighted heavily for use in determining whether to record or not. Forexample, wearable device are available that include sensors such asmoisture, skin conductance, etc. may detect a change in an individual'sskin condition (e.g., a conductance, presence of sweat/moisture).Further signals indicating hormonal changes may be received and coupledwith receipt of facial expressions or physical gestures (of other's inproximity to the individual), the system is operable in real time todetermine the individual's resultant emotional state.

In an example embodiment, a received biometric signal is more heavilyweighted and may be given a top priority in processing as the systemautomatically responds by instantly correlating a biometric signature toan emotional state of the individual. Generally, receipt of a biometricsignal, e.g., indicating a change in the individual's hormone levels, orreceipt of an electrical signal or video signal operate on short/fastertime scales, as compared to the receipt of an audio signal (forexample). Receipt of these types of signals are given a higherprocessing priority, and thus may be more heavily weighted as a factorin determining the individual's emotional state. Other attributes of thereceived sensed signals may be used to assign weights. For example, thefrequency of the signal (e.g., how often the signal is being receivedfrom a sensor), a quality of signal (e.g., noisy or not), an intensitylevel of signal, and/or a type of signal (e.g., analog or digital)received, may all be used in applying a weighting factor to theemotional state determination at step 132.

Thus, at 132, in one embodiment, besides determining if received orassociated with events of a shorter time scale or longer time scale,internal or external factors and their associated information may beweighted more or less, depending upon these attributes of the signalsreceived. For example, a received video signal and its visual content,or sensing events and corresponding inputs at a faster time scale (e.g.,a flash of lightning) may be given a greater processing weight, andtriggers a faster response processing than if/when a corresponding audiosegment (e.g., a received thunder sound) is received/processed. That is,by the time the audio signal is received, a biometric signal change mayhave already been sensed or detected by querying a sensor, and a/thecorresponding visual input may have already been received and processedfirst and given greater weight in determining the emotional state orresponsive action. In the case of an audio signal, it may not benecessary to use its information at all, or it may be given a much lowerweight in determining an emotional state.

Thus, according to a time scale, the time of the signal's receipt (orthe signal's importance (e.g., biometric)) may dictate the logicemployed by the processor; and the logic employed by the processor mayassign a weight to these additional external or internal factors(inputs) in determining an emotional state and the received signalattributes.

In one embodiment, a weight may be applied to a received emotional stateof another individual(s). For example, if an occurrence of an event orrelated external factor signals are detected in a particular locality,proximity or direction (e.g., left side) from an individual, then inputsignals from that side of the user may be given a greater weight indetermining the emotional state of the individual (e.g., give greaterweight to other individuals' emotional state received from individualslocated to the left of the individual).

It is understood that the processor device in this determination ofemotional state or mood may implement a trained model, which modellearns to correlate/associate individual biometric signatures and theindividual's context with an emotional or cognitive state (e.g., scared,intrigued, attentive) and/or apply a weight to the input information forprocessing.

Then, at 134, FIG. 2, a determination is made as to whether any changehas been detected in that individual's emotional state. If after all thereceived inputs are processed it is determined that there is no changein emotional state, the process will return to step 102, where furtherbiometric, external and/or internal factors may be received. Forexample, an individual may always be in an agitated state, and theinputs processed may or may not warrant the triggering of the recordingof the event if the user's heart rate is detected as increasing or theuser is detected as sweating. However, with a determined change in anindividual's emotional state at 134, the process proceeds to step 138where a determination is made as to whether the detected change in anindividual's emotional state is significant enough to warrant atriggering of the recording. If the determined change in emotional stateis not significant enough to warrant triggering of the recording thenthe process again will return to step 102, where further biometric,external and/or internal factors may be received/processed. Otherwise,if the determined change in the individual's emotional state issignificant enough to warrant triggering of the recording, then theprocessor device will generate signals for input to the recording deviceto initiate at 140 the recording and storing of the event data, and orconduct a save of already buffered recorded contents.

With respect to the recording devices, as mentioned, these may includeone or more video, audio or still image capture devices in oneembodiment. For example, the processing device may be configured totrigger and initiate recording at one or multiple recording devicesemployed, and store the recording(s). For example, at a sports event,there may be multiple video cameras that may be triggered by actions ofthe processor to obtain multiple views of a significant event that haschanged the emotional state of users.

In one embodiment, the detection of an occurrence of an event or relatedexternal factor detected in a particular locality, proximity ordirection may also be used to determine a focus area or direction inwhich the recording device is to record a particular event (e.g., videorecord the left side of an individual given the emotion statesdetermined at that locality/direction). Further, an auto-zoom and focusfeature(s) for recording the event may be inherent to the recordingdevice. For a zoom determination, there may be additional inputsrequired for the recording device to perform the zoom. For example, in acrowd of a large event at a stadium, emotional states may be received ina focused area or locality and, the processor may make a determinationto zoom-in a video recording device(s) for recording an event occurringat the locality where many users having increased emotional state changedeterminations. Thus, crowd reactions in a focused area may be used as atrigger point for the device to record at a zoomed-in level.

In one embodiment, a recording device notebook or a smartphone, or likedevice equipped with infrared cameras, may be used to obtainsignals/images of an emissivity of an area, and responsively create athermal map which may be used to trigger an area of focus for therecording device, e.g., a live camera recording device can be used tosee in the dark—such as if the night vision goggles are used. In oneembodiment, the recording device may lie in an idle state (from being inan active device state), or lie in sleep/dormant state until triggeredin which case it is brought to a recording state faster. In the case ofa networked device, the recording devices may exist in a state ready torecord when prompted manually or automatically with signals.

In one embodiment, when the recording device is on, it may beautomatically configured to be continuously in a recording mode, inwhich embodiment audio or audio/visual content signals may be receivedby a recording device that is continuously on, and these contents stored(e.g., buffering in a memory storage medium). In such embodiment, therecording device may correspondingly purge the recorded/stored bufferedcontents (e.g., by automatically deleting the content) unless a signalform the processor device is received to save contents, or the recordingis manually controlled to record and save. This recording device mayalways be on, and when a button on the device is pressed, the devicewill respond by storing (saving) the prior amount of seconds worth ofthe recorded contents stored already by a buffer. For example, prior tobeing turned on or awoken, the device may record and store signalsrepresentative of the event data temporarily in a device buffer. Thus,when the recording device is manually operated, e.g., pressed at timet₀, or when triggered automatically by a processor or like controldevice, the recording device responds by initiating and storing thebuffered data from the memory storage recorded from a prior amount oftime, t_(0 minus a few seconds). Thus, for example, the device may be ofa size sufficient to store prior 10 seconds (for example) amount's worthof buffered recording and may buffer/record automatically a time 10seconds prior to the system sensing and receiving signals associatedwith an event at t₀; and when triggered by a processor or manually, thedevice will save the recorded content obtained from the prior time t⁻¹⁰.A mobile phone such as a smartphone may be equipped with suchrecording/buffering technology.

In one embodiment, a locality or direction and an applied weighting(e.g., putting more emphasis of people in a locality or direction) maybe used to determine where/when and how to record. As external factorsignal inputs (e.g., received audio or video signal) may be weightedmore heavily as being applied to associate the event in a particulardirection or locality relative to an individual (as may be identifiedfrom the received signals of an event), the processor logic employed maygenerate signals for the recording device to focus and/or zoom arecording in the particular direction or locality from where the soundor event is sensed relative to the individual's location.

For example, a determination may be made at step 140, FIG. 2 as to theorigin or locality of the received input signal associated with theevent. For example, an external factor input indicating a soundphysically originating on one side of an individual, or a sensed signal(gesture) physically coming from another user at one side of anindividual (e.g., at a left side) will be given emphasis in and the datasignals and may be weighted appropriately in determining a recordingdevice type or location direction (e.g., left side) with which recordingof the event is to be obtained (e.g., at the left side of the user).Thus, a weighting factor is applied to the data associated with theinput biometric or gesture signal.

In a preferred embodiment, the correlation between biometric signaturesto emotional manifestation is unique to the individual. Such uniquenessis determined by training and calibration of a model based on a set ofevents recreated from memory (e.g., visual imagery, recall from ownmemory of experience) and on-going training and calibration based onnovel events.

Novel events can be automatically identified by the system as emotionalprofiles not yet correlated between individual and event subject, aswell as novel events manually identified by the individual to thesystem.

Turning now to FIG. 3, the system is a cognitive system that learns byexperience. FIG. 3 depicts operations 200 performed by the system 10 totrain a model for use in correlating the individual's biometricsignatures and context to that individual's emotional state.

The operations 200 depicted in FIG. 3 includes, at a step 205, receivingand/or obtaining an individual i's biometric signals/signatureassociated with i's detected emotional state. This may include receiptof an individual i's response to live event stimuli.

Concurrently, or subsequently, the system may receive (or the individuali may provide to the system) that individual's environmental context orcognitive context data for association with the biometric data received.For example, at 210, FIG. 3, the user may input, and the system 10 mayfurther receive individual i's cognitive and/or environmental datainformation for input into the computer system to the extent it isavailable.

Alternatively, or in addition, as shown in FIG. 3, in order to createthe correlation model, further inputs may be received to train thecorrelation model. For example, at 215, the individual i may be furtherprovided with simulated context and stimuli in order to elicitindividual i's emotional response for use in creating the model. Theadditional context information may include: a user's profile informationor context data associated with that user, data associated with thatuser's environment, and user cognitive context data.

Based on these inputs: biometric data, and individual environmental orcognitive context inputs, the processor device 25 of the system 10performs specific method steps to train a emotional statecorrelation/prediction model. It is understood that the training is notlimited to live event stimuli; previously recorded data may be analyzedfor significance based on interactive feedback form individual i, (suchas but not limited to, live sensory, biometric, or emotional feedbackdata).

Thus, assuming there is enough data received to train and contribute tothe model, the process proceeds to step 230, FIG. 3 in order toimplement functions and routines in the processing engine to map thereceived user biometrics and context data to emotional states and trainthe model.

For example, the functions and procedures in the program code mayimplement a regression modeling technique to map user i's biometricdata, context data and other user input data (e.g., external and/orinternal factor inputs) to one or more baseline emotional states.

In one embodiment, a training algorithm may be employed that implementsmachine learning (e.g., supervised or semi-supervised) techniques, sothat the system becomes a cognitive system. Finally, at 235, the systemgenerates a correlation model for subsequent use to predict individuali's emotional state based on received inputs. Thus, system 10 elementsmay be used in training the system for subsequent identification andcorrelation between the sensed biometric and factors and a resultingemotional state.

By training of an individual's personal biometric signals when thatperson is of a particular emotional state, e.g., curious, interested,scared, agitated, etc., the system 10 learns what sensed signals willtrigger the response (and based on the received signals will quicklydetermine the emotional state and initiate appropriate actions, e.g.,record).

Turning now to FIG. 4, there is depicted a method for sharing recordedevents 300 performed at the analytics processor 50 in the network orcloud network 90. In view of FIG. 1, in operation, while an event isoccurring, the wearable sensing and associated transmitting devicecontinuously senses the individual user(s), and obtains informationabout the immediate locality or surroundings and would process the wiredor wireless signals 16, e.g., at a processor device 25 local to theuser, or otherwise, transmit a set of signals 15 to a processing device35 remotely located, e.g., via wireless communications to the network40, to initiate the recording of the live event 75. As an example, inone embodiment, the system 10 may sense that a car operated by theindividual is swerving on a three lane highway, and as the individualuser drives, a cognitive live recording of the event is adapted forrecording as it is happening. That is, the recording is adaptable to theparticular environment as real-time information is being received totrigger recordings.

Once obtained, the recorded event is stored as media data on a localstorage media, e.g., associated with the recording device 30, or in amemory storage device on the network 40.

In one embodiment, as shown in FIG. 1, if the device is on a networksuch as Wi-Fi, a “4G” network, or the like, the recorded event could beuploaded to a secured cloud network 90 employing an analytics processor50 (e.g., at a server device) by communications the recorded event datausing standard TCP/IP protocols and optionally, over a securecommunications link, e.g., using a secure sockets layer, or similarcommunications protocol. The server device may be configured to analyzethe video with analytics and tag or annotate key features/personnel asdescribed herein. Thus, a first step 302 is a loop that is idle untilthe recorded live event data is received by one or more processordevices 50 for analytics processing, which is performed at 305. In analternate embodiment, the analytics processor employed may be the sameprocessor 25 or 35 used in determining an emotional state and triggerrecording.

Thus, in one embodiment, analytics processing at 305 could be performedfor characterizing the recorded event as being personal to theindividual and/or work-related.

The system analytics processing such as performed at 305, responds tothe determined emotional state for triggering the recording, and in oneembodiment, may specify additional metadata (e.g., an annotation) forassociation with the recorded event. Thus, the analytics employed at 305include a triggering of an annotation or tagging the recording data withadditional information for a reader or viewer of the recorded event. Arecording of an event may be annotated and the user (individual) and theemotional state may be given as part of the annotation. In an example,cognitive state/emotional states of plural individual students' in aclass may be used to annotate the recorded event, and used for exampleto determine the teacher's class materials or teacher's effectiveness.

In one embodiment, pixel recreation may be used as a post-processingtechnique to clean up images or video content. Use of interpolation orextrapolation may be used to fix pixels to clear an image prior to beingshared or analyzed.

Analytics logic employed at 305 may be programmed to process and learn,e.g., such as by supervised or unsupervised training, to decide who toshare the recorded event data with; and, in other embodiments, decidehow to annotate the recorded event for the particular viewers to whomthe content is shared. The analytics for sharing in the system willoperate on the recorded event data received from the recording device,and stored/saved in the buffer. The logic used in determining whether totrigger the recording, i.e., by the emotional state determination, maybe used by the analytics, e.g., at 310, FIG. 3, to determine how toshare.

Thus, the system analytics may learn to whom the recorded event shouldbe sent to, e.g., predict that the particular type of event would betargeted to a family member, versus a co-worker. The system analytics at310 may further recommend to the individual certain individual(s) orgroup of people who may receive a recorded event, or determine that therecorded event and corresponding annotation of a mental state should notbe shared at all. This may be correlated to previous individualbehavior, e.g., certain contexts/events may be detected/recorded andprior shared with family members may be used to train the systemanalytics. Thus, the trained system may respond by predicting when a newreceived recorded event would be suitable for certain family members,and for example, recommend to the individual to share the event withfamily members.

In one embodiment, the system may consult with the individual prior tosharing, or alternately, the system analytics may automatically receivethe recorded content and share the recorded content without user input.Thus, in one embodiment, the user is given the option to review theannotation or metadata of the recording (e.g., based on the determinedemotional state). Thus, responsive to the applied analytics and inresponse to a received recommendation at the individual how the recordedvideo event may be characterized/annotated, and to which other peoplethe annotated recorded event may be forwarded to for sharing, the methodproceeds to step 315 to obtain the individual's acceptance of therecommended annotation or characterization of the recorded eventmetadata, and/or the provide the authorization to share with therecommended people.

In one embodiment, after receiving the authorization, and based on thatcharacterization, the method at 320 performs either sharing (such as byfeeding) the recorded event into either a user's personal social networkcircle (e.g., Facebook® 60 a, Twitter® 60 b, Myspace®, Instagram®,Twitter®, Google®, etc.,) or a work circle (e.g., using IBM Connections,etc.), a user selected group, or each of these.

The analytics processing 300 provides an output by the system 10 thatincludes: a go/no-go sharing determination, and a high-level synopsis,(i.e., annotations) relevant to reasons why this recorded event is ofinterest, or is being shared, such as by including one or more of: asummary of emotional state, and summary of biometrics and/or other inputconditions warranting the sharing.

FIG. 5 illustrates one embodiment of an exemplary hardware configurationof a computing system 400 programmed to perform the method steps forimplementing a cognitive event determining and sharing service asdescribed herein with respect to FIGS. 2-4. The hardware configurationpreferably has at least one processor or central processing unit (CPU)411. The CPUs 411 are interconnected via a system bus 412 to a randomaccess memory (RAM) 414, read-only memory (ROM) 416, input/output (I/O)adapter 418 (for connecting peripheral devices such as disk units 421and tape drives 440 to the bus 412), user interface adapter 422 (forconnecting a keyboard 424, mouse 426, speaker 428, microphone 432,and/or other user interface device to the bus 412), a communicationadapter 434 for connecting the system 400 to a data processing network,the Internet, an Intranet, a local area network (LAN), etc., and adisplay adapter 436 for connecting the bus 412 to a display device 438and/or printer 439 (e.g., a digital printer of the like).

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions may also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

It is understood that although this disclosure includes a detaileddescription on cloud environment 90 for analytics computing of therecorded live event for an individual, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 6, a schematic of an example of a cloud computingnode is shown. Cloud computing node 500 is only one example of asuitable cloud computing node and is not intended to suggest anylimitation as to the scope of use or functionality of embodiments of theinvention described herein. Regardless, cloud computing node 500 iscapable of being implemented and/or performing any of the functionalityset forth hereinabove.

In cloud computing node 500 there is a computer system/server 512, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 512 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 512 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 512 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 6, computer system/server 512 in cloud computing node500 is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 512 may include, but are notlimited to, one or more processors or processing units 516, a systemmemory 528, and a bus 518 that couples various system componentsincluding system memory 528 to processor 516.

Bus 518 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system/server 512 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 512, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 528 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 530 and/or cachememory 532. Computer system/server 512 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 534 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 518 by one or more datamedia interfaces. As will be further depicted and described below,memory 528 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 540, having a set (at least one) of program modules 542,may be stored in memory 528 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 542 generally carry out the functionsand/or methodologies of embodiments of the invention as describedherein.

Computer system/server 512 may also communicate with one or moreexternal devices 514 such as a keyboard, a pointing device, a display524, etc.; one or more devices that enable a user to interact withcomputer system/server 512; and/or any devices (e.g., network card,modem, etc.) that enable computer system/server 512 to communicate withone or more other computing devices. Such communication can occur viaInput/Output (I/O) interfaces 522. Still yet, computer system/server 512can communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 520. As depicted, network adapter 520communicates with the other components of computer system/server 512 viabus 518. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 512. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 7, illustrative cloud computing environment 90 isdepicted. As shown, cloud computing environment 90 comprises one or morecloud computing nodes 500 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 504A, desktop computer 504B, laptop computer 504C,and/or automobile computer system 504N may communicate. Nodes 500 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 90 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 504A-Nshown in FIG. 7 are intended to be illustrative only and that computingnodes 500 and cloud computing environment 90 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 8, a set of functional abstraction layers providedby cloud computing environment 90 (FIG. 7) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 8 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 550 includes hardware and softwarecomponents. Examples of hardware components include mainframes, in oneexample IBM® zSeries® systems; RISC (Reduced Instruction Set Computer)architecture based servers, in one example IBM pSeries® systems; IBMxSeries® systems; IBM BladeCenter® systems; storage devices; networksand networking components. Examples of software components includenetwork application server software, in one example IBM WebSphere®application server software; and database software, in one example IBMDB2® database software. (IBM, zSeries, pSeries, xSeries, BladeCenter,WebSphere, and DB2 are trademarks of International Business MachinesCorporation registered in many jurisdictions worldwide).

Virtualization layer 552 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers;virtual storage; virtual networks, including virtual private networks;virtual applications and operating systems; and virtual clients.

In one example, management layer 554 may provide the functions describedbelow. Resource provisioning provides dynamic procurement of computingresources and other resources that are utilized to perform tasks withinthe cloud computing environment. Metering and Pricing provide costtracking as resources are utilized within the cloud computingenvironment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal provides access to the cloud computing environment forconsumers and system administrators. Service level management providescloud computing resource allocation and management such that requiredservice levels are met. Service Level Agreement (SLA) planning andfulfillment provide pre-arrangement for, and procurement of, cloudcomputing resources for which a future requirement is anticipated inaccordance with an SLA.

Workloads layer 556 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation; software development and lifecycle management; virtualclassroom education delivery; data analytics processing; transactionprocessing; and cognitive recording of live events and sharing.

While various embodiments are described herein, it will be appreciatedfrom the specification that various combinations of elements, variationsor improvements therein may be made by those skilled in the art, and arewithin the scope of the invention. In addition, many modifications maybe made to adapt a particular situation or material to the teachings ofthe invention without departing from essential scope thereof. Therefore,it is intended that the invention not be limited to the particularembodiment disclosed as the best mode contemplated for carrying out thisinvention, but that the invention will include all embodiments fallingwithin the scope of the appended claims.

What is claimed is:
 1. An apparatus for cognitive recording and sharingof live events comprising: a processing unit; a recording device torecord a live event; one or more biometric sensors, each configured forobtaining a biometric signal data from an individual; one or moreexternal sensors, each configured for obtaining external signal datafrom an environment in proximity to the individual, the external signaldata comprising data about one or more other individuals in proximity tothe individual; a transmitting device for communicating the one or morebiometric signals and external signal data for receipt at the processingunit, the processing unit configured to: obtain a biometric signature ofthe individual based on a received biometric signal data; obtain, basedon the external signal data, an external factor representing one or moreof: an emotional state of, a biometric signature of, and a determinedprecognition input of the one or more other individuals in proximity tothe individual; determine the individual's current emotional state basedon said obtained biometric signature in combination with said obtainedexternal factor; and record the live event by said recording device inresponse to said determined emotional state.
 2. The apparatus as claimedin claim 1, configured as a networked sharing device including a furtherprocessing unit configured to: receive the recording of said live event,analyze aspects of said recording, and automatically initiate a sharingof said recording via network connection with other individuals based onsaid analysis.
 3. The apparatus as claimed in claim 1, where theprocessing unit is further configured to: correlate biometric signals ofthe individual with a cognitive state or emotional state of saidindividual.
 4. The apparatus as claimed in claim 3, where the processingunit is further configured to: receive a further input signalrepresenting an internal factor, and use said received internal factorin determining said individual's emotional state.
 5. The apparatus asclaimed in claim 4, where a received signal representing an internalfactor comprises: a signal representing one or more of: a hormone changeand a biochemical change of the individual in a time period before,during, and after determining an emotional state change, a signalrepresenting a detected change in an electromagnetic field in closeproximity to the subject individual in the time period and contextbefore, during, and after determining an emotional state change.
 6. Theapparatus as claimed in claim 1, where the external signal datacomprises one or more of: audible sound, the processor unit furtherconfigured to process said received audible signal and determine one ormore of: a sound frequency, a volume, and a proximity of the soundsource in proximity to the individual; and a signal having light orvisual content, the processor unit further configured to process saidreceived visual content and determine one or more of: a visual lightintensity, a color, and a movement in proximity to the individual. 7.The apparatus as claimed in claim 6, where said processing unit isfurther configured to: apply a weighting to the obtained external factoraccording to a characteristic of the received audible sound or visualcharacteristic, said characteristic including a direction or a localityfrom where a sound or visual content is occurring.
 8. The apparatus asclaimed in claim 7, wherein said processing unit is further configuredto: apply a weighting to the obtained biometric signature, saidweighting being higher than the weighting applied to the obtainedexternal factor.
 9. The apparatus as claimed in claim 1, where theprocessing unit is further configured to: receive further input signalsrepresenting a context of the individual; and use said context of theindividual in determining said individual's emotional state.
 10. Amethod for cognitive recording and sharing of live events comprising:receiving, at a processing device, biometric signal data generated froman individual by one or more biometric sensors and external signal datagenerated from an environment in proximity to the individual by one ormore external sensors, the external signal data comprising data aboutone or more other individuals in proximity to the individual; obtaininga biometric signature of the individual based on a received biometricsignal data; obtaining, based on the external signal data, an externalfactor representing one or more of: an emotional state of, a biometricsignature of, and a determined precognition input of the one or moreother individuals in proximity to the individual; determining, at saidprocessing device, an individual's current emotional state based on saidobtained biometric signature in combination with said obtained externalfactor; and triggering a recording device to record a live eventresponsive to determined emotional state.
 11. The method as claimed inclaim 10, further comprising: configuring a processing device to furtherreceive a recording of said live event, analyzing at said processingdevice, aspects of said recording, and automatically sharing saidrecording via a network connection with other individuals based on saidanalysis.
 12. The method as claimed in claim 10, further comprising:correlating received biometric signals of the individual with acognitive state or emotional state of said individual.
 13. The method asclaimed in claim 12, further comprising: receiving, at the processingunit, a further input signal representing an internal factor, and usingsaid received internal factor in determining said individual's emotionalstate.
 14. The method as claimed in claim 13, where a received signalrepresenting an internal factor comprises: a signal representing one ormore of: a hormone change and a biochemical change of the individual ina time period before, during, and after determining an emotional statechange, a signal representing a detected change in an electromagneticfield in close proximity to the subject individual in the time periodand context before, during, and after determining an emotional statechange.
 15. The method as claimed in claim 10, where the external signaldata comprises one or more of: audible sound, the processor unit furtherconfigured to process said received audible signal and determine one ormore of: a sound frequency, a volume, and a proximity of the soundsource in proximity to the individual; and a signal having light orvisual content, the processor unit further configured to process saidreceived visual content and determine one or more of: a visual lightintensity, a color, and a movement in proximity to the individual. 16.The method as claimed in claim 15, further comprising: applying aweighting to the obtained external factor according to a characteristicof the received audible sound or visual characteristic, saidcharacteristic including a direction or a locality from where a sound orvisual content is occurring.
 17. The method as claimed in claim 16,further comprising: applying a weighting to the obtained biometricsignature, said weighting being higher than the weighting applied to theobtained external factor.
 18. The method as claimed in claim 10, furthercomprising: receiving, at the processing unit, a further input signalrepresenting a context of the individual; and using said context of theindividual in determining said individual's emotional state.
 19. Acomputer program product comprising a non-transitory computer readablemedium embodying computer program instructions being run by a processordevice for causing a computer system to perform method steps forcognitive recording and sharing of live events, said method stepscomprising: receiving biometric signal data generated from an individualby one or more biometric sensors and external signal data generated froman environment in proximity to the individual by one or more externalsensors, the external signal data comprising data about one or moreother individuals in proximity to the individual; obtaining a biometricsignature of the individual based on a received biometric signal data;obtaining, based on the external signal data, an external factorrepresenting one or more of: an emotional state of, a biometricsignature of, and a determined precognition input of the one or moreother individuals in proximity to the individual; determining, at saidprocessing device, an individual's current emotional state based on saidobtained biometric signature in combination with said obtained externalfactor; and triggering a recording device to record a live eventresponsive to determined emotional state.
 20. The computer programproduct as claimed in claim 19, where the external signal data comprisesone or more of: audible sound, the processor unit further configured toprocess said received audible signal and determine one or more of: asound frequency, a volume, and a proximity of the sound source inproximity to the individual; and a signal having light or visualcontent, the processor unit further configured to process said receivedvisual content and determine one or more of: a visual light intensity, acolor, and a movement in proximity to the individual, wherein saidmethod further comprises: apply a weighting to the obtained externalfactor according to a characteristic of the received audible sound orvisual characteristic, said characteristic including a direction or alocality from where a sound or visual content is occurring; and apply aweighting to the obtained biometric signature, said weighting beinghigher than the weighting applied to the obtained external factor.